Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V&apos;s of Big Data: Volume, Velocity and Variety. Traditional data warehouse / business intelligence (DW/BI) architecture assumes certain and precise data pursuant to unreasonably large amounts of human capital spent on data preparation, ETL/ELT and master data management. Yet the big data revolution forces us to rethink the traditional DW/BI architecture to accept massive amounts of both structured and unstructured data at great velocity. By definition, unstructured data contains a significant amount of uncertain and imprecise data. For example, social media data is inherently uncertain.Considering variety and velocity of big data, an organization can no longer commit time and resources on traditional ETL/ELT and data preparation to clean up the data to make it certain and precise for analysis. While there are tools to help automate data preparation and cleansing, they are still in the pre-industrial age. As a result, organizations must now analyze both structured and unstructured data that is uncertain and imprecise. The level of uncertainty and imprecision varies on a case by case basis yet must be factored. It may be prudent to assign a Data Veracity score and ranking for specific data sets to avoid making decisions based on analysis of uncertain and imprecise data.

Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V&apos;s of Big Data: Volume, Velocity and Variety. Traditional data warehouse / business intelligence (DW/BI) architecture assumes certain and precise data pursuant to unreasonably large amounts of human capital spent on data preparation, ETL/ELT and master data management. Yet the big data revolution forces us to rethink the traditional DW/BI architecture to accept massive amounts of both structured and unstructured data at great velocity. By definition, unstructured data contains a significant amount of uncertain and imprecise data. For example, social media data is inherently uncertain.Considering variety and velocity of big data, an organization can no longer commit time and resources on traditional ETL/ELT and data preparation to clean up the data to make it certain and precise for analysis. While there are tools to help automate data preparation and cleansing, they are still in the pre-industrial age. As a result, organizations must now analyze both structured and unstructured data that is uncertain and imprecise. The level of uncertainty and imprecision varies on a case by case basis yet must be factored. It may be prudent to assign a Data Veracity score and ranking for specific data sets to avoid making decisions based on analysis of uncertain and imprecise data.

Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V&apos;s of Big Data: Volume, Velocity and Variety. Traditional data warehouse / business intelligence (DW/BI) architecture assumes certain and precise data pursuant to unreasonably large amounts of human capital spent on data preparation, ETL/ELT and master data management. Yet the big data revolution forces us to rethink the traditional DW/BI architecture to accept massive amounts of both structured and unstructured data at great velocity. By definition, unstructured data contains a significant amount of uncertain and imprecise data. For example, social media data is inherently uncertain.Considering variety and velocity of big data, an organization can no longer commit time and resources on traditional ETL/ELT and data preparation to clean up the data to make it certain and precise for analysis. While there are tools to help automate data preparation and cleansing, they are still in the pre-industrial age. As a result, organizations must now analyze both structured and unstructured data that is uncertain and imprecise. The level of uncertainty and imprecision varies on a case by case basis yet must be factored. It may be prudent to assign a Data Veracity score and ranking for specific data sets to avoid making decisions based on analysis of uncertain and imprecise data.

Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V&apos;s of Big Data: Volume, Velocity and Variety. Traditional data warehouse / business intelligence (DW/BI) architecture assumes certain and precise data pursuant to unreasonably large amounts of human capital spent on data preparation, ETL/ELT and master data management. Yet the big data revolution forces us to rethink the traditional DW/BI architecture to accept massive amounts of both structured and unstructured data at great velocity. By definition, unstructured data contains a significant amount of uncertain and imprecise data. For example, social media data is inherently uncertain.Considering variety and velocity of big data, an organization can no longer commit time and resources on traditional ETL/ELT and data preparation to clean up the data to make it certain and precise for analysis. While there are tools to help automate data preparation and cleansing, they are still in the pre-industrial age. As a result, organizations must now analyze both structured and unstructured data that is uncertain and imprecise. The level of uncertainty and imprecision varies on a case by case basis yet must be factored. It may be prudent to assign a Data Veracity score and ranking for specific data sets to avoid making decisions based on analysis of uncertain and imprecise data.

Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V&apos;s of Big Data: Volume, Velocity and Variety. Traditional data warehouse / business intelligence (DW/BI) architecture assumes certain and precise data pursuant to unreasonably large amounts of human capital spent on data preparation, ETL/ELT and master data management. Yet the big data revolution forces us to rethink the traditional DW/BI architecture to accept massive amounts of both structured and unstructured data at great velocity. By definition, unstructured data contains a significant amount of uncertain and imprecise data. For example, social media data is inherently uncertain.Considering variety and velocity of big data, an organization can no longer commit time and resources on traditional ETL/ELT and data preparation to clean up the data to make it certain and precise for analysis. While there are tools to help automate data preparation and cleansing, they are still in the pre-industrial age. As a result, organizations must now analyze both structured and unstructured data that is uncertain and imprecise. The level of uncertainty and imprecision varies on a case by case basis yet must be factored. It may be prudent to assign a Data Veracity score and ranking for specific data sets to avoid making decisions based on analysis of uncertain and imprecise data.

Data Veracity, uncertain or imprecise data, is often overlooked yet may be as important as the 3 V&apos;s of Big Data: Volume, Velocity and Variety. Traditional data warehouse / business intelligence (DW/BI) architecture assumes certain and precise data pursuant to unreasonably large amounts of human capital spent on data preparation, ETL/ELT and master data management. Yet the big data revolution forces us to rethink the traditional DW/BI architecture to accept massive amounts of both structured and unstructured data at great velocity. By definition, unstructured data contains a significant amount of uncertain and imprecise data. For example, social media data is inherently uncertain.Considering variety and velocity of big data, an organization can no longer commit time and resources on traditional ETL/ELT and data preparation to clean up the data to make it certain and precise for analysis. While there are tools to help automate data preparation and cleansing, they are still in the pre-industrial age. As a result, organizations must now analyze both structured and unstructured data that is uncertain and imprecise. The level of uncertainty and imprecision varies on a case by case basis yet must be factored. It may be prudent to assign a Data Veracity score and ranking for specific data sets to avoid making decisions based on analysis of uncertain and imprecise data.

4.
Some BIG facts
• 90% of the data in the world today has been created in the
last two years alone
• IDC Forecasting: The global universe of data will double
every two years, reaching 40,000 exabytes or 40 trillion GB
by 2020
• The Large Hadron Collider near Geneva, Switzerland, will
produce about 15 petabytes of data per year.
• Ancestry.com, the genealogy site, stores around 2.5
petabytes of data.
• The Internet Archive stores around 2 petabytes of data, and
is growing at a rate of 20 terabytes per month.

5.
Some BIG facts – What happens everyday?
• The New York Stock Exchange generates about one
terabyte of new trade data
• Zynga processes 1 Petabyte of content
• 30 billion pieces of content were added to Facebook
• 2 billion videos are watched in Youtube
• 2.5 quintillion bytes of data is created

15.
Big data - Veracity
• This kind of data is often
overlooked
• It is now considered as
important as 3 V’s of Big Data
• Effort to clean up data is rather
not given importance
• Poor data quality costs the U.S.
economy around $3.1 trillions a
year
Source: McKinsey, Gartner, Twitter, Cisco, EMC, SAS, IBM, MEPTEC, QAS